Visualized attribute analysis approach for characterization and quantification of rice taste flavor using electronic tongue

This paper deals with a novel visualized attributive analysis approach for characterization and quantification of rice taste flavor attributes (softness, stickiness, sweetness and aroma) employing a multifrequency large-amplitude pulse voltammetric electronic tongue. Data preprocessing methods including Principal Component Analysis (PCA) and Fast Fourier Transform (FFT) were provided. An attribute characterization graph was represented for visualization of the interactive response in which each attribute responded by specific electrodes and frequencies. The model was trained using signal data from electronic tongue and attribute scores from artificial evaluation. The correlation coefficients for all attributes were over 0.9, resulting in good predictive ability of attributive analysis model preprocessed by FFT. This approach extracted more effective information about linear relationship between electronic tongue and taste flavor attribute. Results indicated that this approach can accurately quantify taste flavor attributes, and can be an efficient tool for data processing in a voltammetric electronic tongue system.     

Polymeric membrane ion-selective and cross-sensitive electrode-based electronic tongue for qualitative analysis of beverages

An electronic tongue based on the sensor array of polymeric membrane ion-selective electrodes combined with pattern recognition tools was applied to qualitative analysis of various brands of orange juice, tonic, and milk. The capability of this device to reliably discriminate between different brands of those products was presented. The tests of the system were performed using products of the same brand, but with different manufacture dates (and thus comparable by the term of taste). The fusion of two types of sensors-classical selective ones and partially selective in one versatile array, and working out the sensor array's response by means of principal component analysis and back propagation neural network methods allowed the discrimination between different brands of various beverages with very high accuracy (90-100%). The real performance of the electronic tongue was evaluated applying testing samples from another manufacture lot, than the samples used in the learning set.     

The recognition of beer with flow-through sensor array based on miniaturized solid-state electrodes

Flow-through electronic tongue based on miniaturized solid-state potentiometric sensors has been developed. A simple technique, i.e. membrane solution casting on the surface of the planar Au transducers was applied for the preparation of classical ion-selective and partially selective microelectrodes, introduced in the flow-through sensor array. The performance of the designed electronic tongue was tested in the qualitative analysis of various brands of beer. Samples of the same brand of beer but with different manufacture dates, originating from different manufacture lots, have been applied in the studies. The combination of PLS and ANN techniques allowed the discrimination between different brands of beer with 83% of correct classifications.     

Non-selective chemical sensors in analytical chemistry: from “electronic nose” to “electronic tongue”

Development, recent historical background and analytical applications of promising sensor instruments based on sensor arrays with data processing by pattern recognition methods have been described. Attention is paid to the “electronic tongue” based on an array of original non-specific (non-selective) potentiometric chemical sensors. Application results for integral qualitative analysis of beverages and for quantitative analysis of biological liquids and solutions, containing heavy metals are reported. Discriminating abilities and precision obtained allow to consider “electronic tongue” as a perspective analytical tool.     

Evaluation of Italian wine by the electronic tongue: recognition, quantitative analysis and correlation with human sensory perception

The electronic tongue based on a sensor array comprising 23 potentiometric cross-sensitive chemical sensors and pattern recognition and multivariate calibration data processing tools was applied to the analysis of Italian red wines. The measurements were made in 20 samples of Barbera d’Asti and in 36 samples of Gutturnio wine. The electronic tongue distinguished all wine samples of the same denomination and vintage, but from different vineyards. Simultaneously the following quantitative parameters of the wines were measured by the electronic tongue with precision within 12%: total and volatile acidity, pH, ethanol content, contents of tartaric acid, sulphur dioxide, total polyphenols, glycerol, etc. The electronic tongue is sensitive to multiple substances that determine taste and flavour of wine and, hence, the system was capable of predicting human sensory scores with average precision of 13% for Barbera d’Asti wines and 8% for Gutturnio wines.     

Electronic tongues and their analytical application

Electronic tongues for liquid analysis, based on the organizational principles of biological sensory systems, developed rapidly during the last decade. A brief historical overview of the research and development in the field of electronic tongue systems is presented. Current achievements of scientific groups working in this field are outlined and critically reviewed. The performance of electronic tongues in quantitative analysis and in classification of multicomponent media is considered. The exciting possibility of establishing a correlation between the output from an electronic tongue and human sensory assessment of food flavour, thereby enabling quantification of taste and flavour, is described. Application areas of electronic tongue systems including foodstuffs, clinical, industrial, and environmental analysis are discussed in depth. Prospective research and development in the field of electronic tongues is discussed.     

Comparison of methods for the processing of voltammetric electronic tongues data

We are making a numerical comparison of various preprocessing strategies for dealing with data from voltammetric electronic tongues in order to reduce the high dimensionality of the response matrices. Different modelling tools are presented and briefly described. We then compare combinations of four preprocessing strategies (principal component analysis, fast Fourier transform, discrete wavelet transform, voltammogram-windowed slicing integral) with four modelling alternatives (principal component regression, partial least squares regression, multi-way partial least squares regression, artificial neural networks) by employing data from a voltammetric bioelectronic tongue, an array formed by enzyme-modified biosensors and applied to the discrimination and quantification of phenolic compounds.

Determination of indoor air quality of a phytosanitary plant

Effective fermentation monitoring is a growing need due to the rapid pace of change in the wine industry, which calls for fast methods providing real time information in order to assure the quality of the final product. The objective of this work is to investigate the potential of non-destructive techniques associated with chemometric data analysis, to monitor time-related changes that occur during red wine fermentation. Eight micro-fermentation trials conducted in the Valtellina region (Northern Italy) during the 2009 vintage, were monitored by a FT-NIR and a FT-IR spectrometer and by an electronic nose and tongue. The spectroscopic technique was used to investigate molecular changes, while electronic nose and electronic tongue evaluated the evolution of the aroma and taste profile during the must-wine fermentation. Must-wine samples were also analysed by traditional chemical methods in order to determine sugars (glucose and fructose) consumption and alcohol (ethanol and glycerol) production. Principal Component Analysis was applied to spectral, electronic nose and electronic tongue data, as an exploratory tool, to uncover molecular, aroma and taste modifications during the fermentation process. Furthermore, the chemical data and the PC1 scores from spectral, electronic nose and electronic tongue data were modelled as a function of time to identify critical points during fermentation. The results showed that NIR and MIR spectroscopies are useful to investigate molecular changes involved in wine fermentation while electronic nose and electronic tongue can be applied to detect the evolution of taste and aroma profile. Moreover, as demonstrated through the modeling of NIR, MIR, electronic nose and electronic tongue data, these non destructive methods are suitable for the monitoring of must-wine fermentation giving crucial information about the quality of the final product in agreement with chemical parameters. Although in this study the measurements were carried out in off-line mode, in future these non destructive techniques could be valid and simple tools, able to provide in-time information about the fermentation process and to assure the quality of wine.     

Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition

Analysis of four Tieguanyin teas from different origins were performed using an electronic tongue, which has significant advantages in terms of accuracy and precision for pattern recognition. Hierarchical cluster analysis and principal component analysis were then applied to identify origins of these teas, and a distinct separation was observed. The back propagation neural network (BPNN) and the back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) were applied to build identification models. The Levenberg-Marquardt training algorithm model outperformed the back propagation neural network, as the identification performances of the former model were 100% in the training and prediction sets when four principal components were used. The results demonstrate that an electronic tongue with pattern recognition is suitable to classify Tieguanyin tea and shows broad potential in food inspection and quality control.     

Electrochemical monitoring of citric acid production by Aspergillus niger

Hybrid electronic tongue was developed for the monitoring of citric acid production by Aspergillus niger. The system based on various potentiometric/voltammetric sensors and appropriate chemometric techniques provided correct qualitative and quantitative classification of the samples collected during standard Aspergillus niger culture and culture infected with yeast. The performance of the proposed approach was compared with the monitoring of the fermentation process carried out using classical methods. The results obtained proved, that the designed hybrid electronic tongue was able to evaluate the progress and correctness of the fermentation process.     

Use of a Voltammetric Electronic Tongue for Detection and Classification of Nerve Agent Mimics

An electronic tongue (ET) based on pulse voltammetry has been used to predict the presence of nerve agent mimics in aqueous environments. The electronic tongue array consists of eight working electrodes (Au, Pt, Ir, Rh, Cu, Co, Ni and Ag) encapsulated on a stainless steel cylinder. Studies including principal component analysis (PCA), artificial neural networks (fuzzy ARTMAP) and partial least square techniques (PLS) have been applied for data management and prediction models. For instance the electronic tongue is able to discriminate the presence of the nerve agent simulants diethyl chlorophosphate (DCP) and diethyl cyanophosphate (DCNP) from the presence of other organophosphorous derivatives in water. Finally, PLS data analysis using a system of 3 compounds and 3 concentration levels shows a good accuracy in concentration prediction for DCP and DCNP in aqueous environments.     

Multicomponent analysis of fermentation growth media using the electronic tongue (ET)

A potentiometric electronic tongue (ET) consisting of eight cross-sensitive chemical sensors and a standard pH electrode has been applied for analysis of simulated fermentation solutions typical for fermentation processes with Aspergillus niger. The electronic tongue has been found capable of simultaneous determination of ammonium, citrate and oxalate in complex media with good precision (typical error within 8%). The system preserved high sensitivity to the targeted substances also in the presence of sodium azide, which is commonly used for suppressing microbial activity in real-world fermentation samples. Sensor performance was fast and reproducible which promises well for routine application of the electronic tongue for fermentation process monitoring.     

Use of copper and gold electrodes as sensitive elements for fabrication of an electronic tongue: Discrimination of wines and whiskies

A low-cost method is proposed to classify wine and whisky samples using a disposable voltammetric electronic tongue that was fabricated using gold and copper substrates and a pattern recognition technique (Principal Component Analysis). The proposed device was successfully used to discriminate between expensive and cheap whisky samples and to detect adulteration processes using only a copper electrode. For wines, the electronic tongue was composed of copper and gold working electrodes and was able to classify three different brands of wine and to make distinctions regarding the wine type, i.e., dry red, soft red, dry white and soft white brands.     

A Voltammetric Electronic Tongue Based on Commercial Screen‐printed Electrodes for the Analysis of Aminothiols by Differential Pulse Voltammetry

A voltammetric electronic tongue has been designed as a proof of concept for the analysis of aminothiols by differential pulse voltammetry and has been tested in ternary mixtures of cysteine (Cys), homocysteine (hCys) and glutathione (GSH). It consists of three screen‐printed electrodes of carbon, carbon nanofibers and gold cured at low temperature. A preliminary calibration study carried out separately for each aminothiol confirmed that, working at an optimal pH value of 7.4, every electrode produces differentiated responses for every analyte (cross‐response). As for the tongue, it was applied to calibration and validation mixtures of Cys, hCys and GSH and provided voltammograms that, baseline‐corrected, normalized and combined in different ways were submitted to partial least squares (PLS) calibration. The calibration models produced good predictions of the concentrations of all three analytes, which suggest that the proposed voltammetric tongue improves the performance of a previous design based on linear sweep voltammetric measurements under acidic conditions.     

Pattern recognition in capillary electrophoresis data using dynamic programming in the wavelet domain

A novel approach for CE data analysis based on pattern recognition techniques in the wavelet domain is presented. Low-resolution, denoised electropherograms are obtained by applying several preprocessing algorithms including denoising, baseline correction, and detection of the region of interest in the wavelet domain. The resultant signals are mapped into character sequences using first derivative information and multilevel peak height quantization. Next, a local alignment algorithm is applied on the coded sequences for peak pattern recognition. We also propose 2-D and 3-D representations of the found patterns for fast visual evaluation of the variability of chemical substances concentration in the analyzed samples. The proposed approach is tested on the analysis of intracerebral microdialysate data obtained by CE and LIF detection, achieving a correct detection rate of about 85% with a processing time of less than 0.3 s per 25,000-point electropherogram. Using a local alignment algorithm on low-resolution denoised electropherograms might have a great impact on high-throughput CE since the proposed methodology will substitute automatic fast pattern recognition analysis for slow, human based time-consuming visual pattern recognition methods.     

Use of sequential injection analysis to construct an electronic-tongue: application to multidetermination employing the transient response of a potentiometric sensor array

An electronic tongue based on the transient response of an array of non-specific-response potentiometric sensors was developed. A sequential injection analysis (SIA) system was used in order to automate its training and operation. The use of the transient recording entails the dynamic nature of the sensor's response, which can be of high information content, of primary ions and also of interfering ions; these may better discriminated if the kinetic resolution is added. This work presents the extraction of significant information contained in the transient response of a sensor array formed by five all-solid-state potentiometric sensors. The tool employed was the Fourier transform, from which a number of coefficients were fed into an artificial neural network (ANN) model, used to perform a quantitative multidetermination. The studied case was the analysis of mixtures of calcium, sodium and potassium. Obtained performance is compared with the more traditional automated electronic tongue using final steady-state potentials.     

Determination of detergents in washing machine wastewater with a voltammetric electronic tongue

A voltammetric electronic tongue (ET) and a conductivity meter were used to predict amounts of detergents in process water from washing machines. The amount of detergent in over sixty samples was also determined by a HPLC reference method. Prediction was more accurate for the electronic tongue, but both techniques could be used. The composition of the detergent, e.g. supporting electrolyte, is an important factor for the ability to predict the detergent quantity by conductivity. Also two different surfactants, alkyl benzyl sulfonate (ABS) and etoxylated fatty alcohol (EOA), were fingerprinted by the HPLC. Their behaviour during the wash cycle differs from each other, ABS rinses away in the same proportions as the supporting electrolyte, but EOA appears to stay within the machine and laundry. Prediction models for ABS are accurate both with ET and conductivity meter, mostly due to the correlation with supporting electrolyte. The behaviour of EOA, with almost no correlation to the supporting electrolyte makes it difficult to predict using conductivity but ET prediction models give promising indications of its capabilities.     

Simultaneous determination of fermented milk aroma compounds by a potentiometric sensor array

The paper reports on the application of an electronic tongue for simultaneous determination of ethanol, acetaldehyde, diacetyl, lactic acid, acetic acid and citric acid content in probiotic fermented milk. The αAstree electronic tongue by Alpha M.O.S. was employed. The sensor array comprised of seven non-specific, cross-sensitive sensors developed especially for food analysis coupled with a reference Ag/AgCl electrode. Samples of plain, strawberry, apple-pear and forest-fruit flavored probiotic fermented milk were analyzed both by standard methods and by the potentiometric sensor array. The results obtained by these methods were used for the development of neural network models for rapid estimation of aroma compounds content in probiotic fermented milk.The highest correlation (0.967) and lowest standard deviation of error for the training (0.585), selection (0.503) and testing (0.571) subset was obtained for the estimation of ethanol content. The lowest correlation (0.669) was obtained for the estimation of acetaldehyde content. The model exhibited poor performance in average error and standard deviations of errors in all subsets which could be explained by low sensitivity of the sensor array to the compound. The obtained results indicate that the potentiometric electronic tongue coupled with artificial neural networks can be applied as a rapid method for the determination of aroma compounds in probiotic fermented milk.     

A voltammetric electronic tongue made of modified epoxy-graphite electrodes for the qualitative analysis of wine

First results are presented of a new voltammetric electronic tongue which employs modified epoxy-graphite electrodes. This analytical tool has been applied to qualitative wine analysis, performing the classification of wine varieties, as well as recognition of the oxygenation effect. In the same way, studies related to the detection of some defects in wine production were also assessed, such as its vinegary taste in open-air contact or the use of excess sulphite preservative. The electronic tongue was formed by five voltammetric electrodes, four of them being bulk-modified with different substances: copper and platinum nanoparticles on one side, and polyaniline and polypyrrole powder on the other. The responses were preprocessed employing Principal Component Analysis (PCA) to visualize and identify distinct episodes. The resulting PCA scores were modelled with an artificial neural network that accomplishes final prediction with the qualitative classification of wines and/or detection of defects.